Results 11 to 20 of about 2,675,626 (288)
Generalized Multiple Importance Sampling [PDF]
Statistical Science, 2019 Importance Sampling methods are broadly used to approximate posterior distributions or some of their moments. In its standard approach, samples are drawn from a single proposal distribution and weighted properly. However, since the performance depends on the mismatch between the targeted and the proposal distributions, several proposal densities are ...
Elvira, Víctor +3 more
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Layered adaptive importance sampling [PDF]
Statistics and Computing, 2016 Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples.
Martino, Luca +3 more
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Importance Nested Sampling and the MultiNest Algorithm
The Open Journal of Astrophysics, 2019 Bayesian inference involves two main computational challenges. First, in estimating the parameters of some model for the data, the posterior distribution may well be highly multi-modal: a regime in which the convergence to stationarity of traditional ...
Farhan Feroz +3 more
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Adaptive Multiple Importance Sampling [PDF]
Scandinavian Journal of Statistics, 2012 Abstract. The Adaptive Multiple Importance Sampling algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling (IS) scheme. The difference with earlier adaptive IS implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each ...
Jean Marie Cornuet +3 more
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, 2023 Stein discrepancies have emerged as a powerful tool for retrospective improvement of Markov chain Monte Carlo output. However, the question of how to design Markov chains that are well-suited to such post-processing has yet to be addressed. This paper studies Stein importance sampling, in which weights are assigned to the states visited by a $Π ...
Wang, Congye +3 more
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Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization
Frontiers in Applied Mathematics and Statistics, 2018 In this paper we develop an adaptive dual free Stochastic Dual Coordinate Ascent (adfSDCA) algorithm for regularized empirical risk minimization problems. This is motivated by the recent work on dual free SDCA of Shalev-Shwartz [1].
Xi He, Rachael Tappenden, Martin Takáč
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Heretical Multiple Importance Sampling [PDF]
IEEE Signal Processing Letters, 2016 Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the importance weights assigned to each sample have been recently proposed, with the so-called deterministic mixture (DM) weights providing the best performance in terms of variance,
Elvira, Víctor +3 more
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Quantile estimation with adaptive importance sampling [PDF]
, 2010 We introduce new quantile estimators with adaptive importance sampling. The adaptive estimators are based on weighted samples that are neither independent nor identically distributed.
Egloff, Daniel, Leippold, Markus
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Adaptive Importance Sampling in General Mixture Classes [PDF]
, 2008 In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion.
A. Doucet +18 more
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Assessing Asset-Liability Risk with Neural Networks
Risks, 2020 We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is ...
Patrick Cheridito +2 more
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